Predicting wireless quality of service (qos) for connected vehicles

ABSTRACT

A method of a route prediction system utilizing real-time mobile communication network data. The method includes receiving a route request originating from a connected vehicle, the route request identifying a route, determining segments for the route, localizing mobile communication network resources to the segments, determining key performance indicators for the segments based on at least a current offline model, and sending predicted service level indicators (SLIs) for the segments to the connected vehicle.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.63/012,042, filed 17 Apr. 2020, which is hereby incorporated byreference.

TECHNICAL FIELD

Embodiments of the invention relate to the field of connected vehiclenavigation using mobile networks; and more specifically, to a method andapparatus for providing improved routing of connected vehicles usingreal time mobile communication network information.

BACKGROUND ART

Autonomous vehicles navigate independent of local manual control. An‘autonomous’ vehicle, as used herein refers to a vehicle that isself-governed. Any type of vehicle can be an autonomous vehicleincluding cars, trucks, drones, planes, boats, and similar vehicles ofany size or type. Autonomous operation generally indicates that theautonomous vehicle is able to navigate in an uncontrolled navigationalenvironment where there may be significant uncertainties such that theautonomous vehicle is able to compensate for these uncertainties and forsystem issues or failures without human intervention. Levels ofautomation can be categories such as the levels 0-5 defined by Societyof Automotive Engineers (SAE) International.

The autonomous vehicles utilize a large amount of data to makenavigation decisions. Some of this data is collected local to thevehicle via sensors, while other data is received via mobilecommunication systems. Local sensors can include lidar, stereo vision,Global Positioning System (GPS), inertial measurement units (IMUs) andsimilar sensors. The locally collected data can be processed by thenavigation algorithms of the autonomous vehicle as input along withlocally stored terrain, map, geospatial, or similar data. The navigationalgorithms can control the various systems of the autonomous vehicle,such as propulsion systems, steering systems, and similar systems. Thenavigation algorithms can also utilize computing resources and data thatis not local to the autonomous vehicle.

The autonomous vehicle that is connected by communications networks withexternal computing and data resources is a type of ‘connected vehicle,’which can receive and transmit large amounts of data between the onboardcomputing systems of the autonomous vehicle and remote computing systemsusing mobile communication systems. The data received can include dataabout local conditions such as weather, traffic, road conditions andsimilar data. The data transmitted can include data collected by sensorsof the autonomous vehicle. However, exchanging this data with externalcomputing resources can be interrupted or fall below necessary qualityof service (QoS) levels where the mobile communication network has pooror no coverage making reliance on external computing resources and dataproblematic.

SUMMARY

In a first embodiment, a method of a route prediction system utilizesreal-time mobile communication network data. The method includesreceiving a route request originating from a connected vehicle, theroute request identifying a route, determining segments for the route,localizing mobile communication network resources to the segments,determining key performance indicators for the segments based on atleast a current offline model, and sending predicted service levelindicators (SLIs) for the segments to the connected vehicle.

In another embodiment, a network device is configured to implement themethod of the route prediction system utilizing real-time mobilecommunication network data. The network device includes a non-transitorymachine-readable storage medium having stored therein a route predictionblock, and a processor coupled to the non-transitory machine-readablestorage medium. The processor executes the route prediction block. Theroute prediction block receives a route request originating from aconnected vehicle, the route request identifying a route, determinessegments for the route, localizes mobile communication network resourcesto the segments, determines key performance indicators for the segmentsbased on at least a current offline model, and sends predicted servicelevel indicators (SLIs) for the segments to the connected vehicle.

In a further embodiment, the networking device is configured to executea plurality of virtual machines, the plurality of virtual machinesimplementing network function virtualization (NFV). The network deviceincludes a non-transitory machine-readable storage medium having storedtherein a route prediction block, and a processor coupled to thenon-transitory machine-readable storage medium. The processor executesat least one of the plurality of virtual machines. The at least one ofthe plurality of virtual machines executes the route prediction block.The route prediction block receives a route request originating from aconnected vehicle, the route request identifying a route, determinessegments for the route, localizes mobile communication network resourcesto the segments, determines key performance indicators for the segmentsbased on at least a current offline model, and sends predicted servicelevel indicators (SLIs) for the segments to the connected vehicle.

In one embodiment, an electronic device in a software defined networking(SDN) network includes a plurality of data plane devices. The electronicdevice includes a non-transitory machine-readable storage medium havingstored therein a prediction service, and a processor coupled to thenon-transitory machine-readable storage medium. The processor executesthe prediction service. The prediction service receives a route requestoriginating from a connected vehicle, the route request identifying aroute, determines segments for the route, localizes mobile communicationnetwork resources to the segments, determines key performance indicatorsfor the segments based on at least a current offline model, and sendspredicted service level indicators (SLIs) for the segments to theconnected vehicle.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention may best be understood by referring to the followingdescription and accompanying drawings that are used to illustrateembodiments of the invention. In the drawings:

FIG. 1 is a diagram of one embodiment of an overview of the predictionsystem.

FIG. 2A is a diagram of one embodiment of the online process of theprediction system.

FIG. 2B is a flowchart of one embodiment of the online processimplemented by a route prediction block (RPB).

FIG. 3A is a diagram of an offline process for the prediction system.

FIG. 3B is a flowchart of one embodiment of the operation of the RPBimplementing the offline process.

FIG. 4 is a diagram of one example of a propagation model.

FIG. 5 is a flowchart of one example embodiment of a process offiltering the network data to be used by the offline process.

FIG. 6 is a flowchart of one embodiment of a process of a mappingfunction to correlate mobile communication network data to routes.

FIG. 7 is a flowchart of one embodiment of the offline SLI modelingimplementation.

FIG. 8 is a flowchart of one example embodiment of feedback function forthe online SLI scoring implementation.

FIG. 9 is a flowchart of one example embodiment of an offline retrainingprocess.

FIG. 10 is a diagram of one embodiment of a system for brokeringinformation between multiple Mobile Network Operator (MNO) sources.

FIG. 11 is a flowchart of one embodiment of the process of integratingan Autonomous Driving Fleet (ADF) with the prediction system.

FIG. 12 is a flowchart of one embodiment of the online processimplemented at a cloud platform (CP) to support information brokering.

FIG. 13 is a flowchart of one example embodiment of a process forhandling route information at a CP for the prediction system and serviceto support information brokering.

FIG. 14 is a diagram of one embodiment of a cloud implementation of theprediction system and service.

FIG. 15A illustrates connectivity between network devices (NDs) withinan exemplary network, as well as three exemplary implementations of theNDs, according to some embodiments of the invention.

FIG. 15B illustrates an exemplary way to implement a special-purposenetwork device according to some embodiments of the invention.

FIG. 15C illustrates various exemplary ways in which virtual networkelements (VNEs) may be coupled according to some embodiments of theinvention.

FIG. 15D illustrates a network with a single network element (NE) oneach of the NDs, and within this straightforward approach contrasts atraditional distributed approach (commonly used by traditional routers)with a centralized approach for maintaining reachability and forwardinginformation (also called network control), according to some embodimentsof the invention.

FIG. 15E illustrates the simple case of where each of the NDs implementsa single NE, but a centralized control plane has abstracted multiple ofthe NEs in different NDs into (to represent) a single NE in one of thevirtual network(s), according to some embodiments of the invention.

FIG. 15F illustrates a case where multiple VNEs are implemented ondifferent NDs and are coupled to each other, and where a centralizedcontrol plane has abstracted these multiple VNEs such that they appearas a single VNE within one of the virtual networks, according to someembodiments of the invention.

FIG. 16 illustrates a general purpose control plane device withcentralized control plane (CCP) software 1650), according to someembodiments of the invention.

DETAILED DESCRIPTION

The following description describes methods and apparatus for improvingthe field of wireless mobile communication systems and connectedvehicles. Connected vehicles can include any type of vehicle with acapability to communicate with external computing resources. Connectedvehicles can include autonomous vehicles such as unmanned aircraft anddriverless cars and trucks, autonomous sidewalk robots, vehicles orapplications that consume media or other data sources in real time andwhile on the move. The embodiments provide methods and apparatus foraccelerating the advent of connected vehicles by enabling greatercertainty about the level of mobile service that the connected vehicleswill experience on different routes of potential trips through apartnership with mobile communication network operators to use real timedata from the mobile communication networks to provide connectedvehicles with route and trip-specific data including road-focusedprediction of quality of service along different potential routes,segments of routes or trips for the connected vehicles.

In the following description, numerous specific details such as logicimplementations, opcodes, means to specify operands, resourcepartitioning/sharing/duplication implementations, types andinterrelationships of system components, and logicpartitioning/integration choices are set forth in order to provide amore thorough understanding of the present invention. It will beappreciated, however, by one skilled in the art that the invention maybe practiced without such specific details. In other instances, controlstructures, gate level circuits and full software instruction sequenceshave not been shown in detail in order not to obscure the invention.Those of ordinary skill in the art, with the included descriptions, willbe able to implement appropriate functionality without undueexperimentation.

References in the specification to “one embodiment,” “an embodiment,”“an example embodiment,” etc., indicate that the embodiment describedmay include a particular feature, structure, or characteristic, butevery embodiment may not necessarily include the particular feature,structure, or characteristic. Moreover, such phrases are not necessarilyreferring to the same embodiment. Further, when a particular feature,structure, or characteristic is described in connection with anembodiment, it is submitted that it is within the knowledge of oneskilled in the art to affect such feature, structure, or characteristicin connection with other embodiments whether or not explicitlydescribed.

Bracketed text and blocks with dashed borders (e.g., large dashes, smalldashes, dot-dash, and dots) may be used herein to illustrate optionaloperations that add additional features to embodiments of the invention.However, such notation should not be taken to mean that these are theonly options or optional operations, and/or that blocks with solidborders are not optional in certain embodiments of the invention.

In the following description and claims, the terms “coupled” and“connected,” along with their derivatives, may be used. It should beunderstood that these terms are not intended as synonyms for each other.“Coupled” is used to indicate that two or more elements, which may ormay not be in direct physical or electrical contact with each other,co-operate or interact with each other. “Connected” is used to indicatethe establishment of communication between two or more elements that arecoupled with each other.

An electronic device stores and transmits (internally and/or with otherelectronic devices over a network) code (which is composed of softwareinstructions and which is sometimes referred to as computer program codeor a computer program) and/or data using machine-readable media (alsocalled computer-readable media), such as machine-readable storage media(e.g., magnetic disks, optical disks, solid state drives, read onlymemory (ROM), flash memory devices, phase change memory) andmachine-readable transmission media (also called a carrier) (e.g.,electrical, optical, radio, acoustical or other form of propagatedsignals—such as carrier waves, infrared signals). Thus, an electronicdevice (e.g., a computer) includes hardware and software, such as a setof one or more processors (e.g., wherein a processor is amicroprocessor, controller, microcontroller, central processing unit,digital signal processor, application specific integrated circuit, fieldprogrammable gate array, other electronic circuitry, a combination ofone or more of the preceding) coupled to one or more machine-readablestorage media to store code for execution on the set of processorsand/or to store data. For instance, an electronic device may includenon-volatile memory containing the code since the non-volatile memorycan persist code/data even when the electronic device is turned off(when power is removed), and while the electronic device is turned onthat part of the code that is to be executed by the processor(s) of thatelectronic device is typically copied from the slower non-volatilememory into volatile memory (e.g., dynamic random access memory (DRAM),static random access memory (SRAM)) of that electronic device. Typicalelectronic devices also include a set of one or more physical networkinterface(s) (NI(s)) to establish network connections (to transmitand/or receive code and/or data using propagating signals) with otherelectronic devices. For example, the set of physical NIs (or the set ofphysical NI(s) in combination with the set of processors executing code)may perform any formatting, coding, or translating to allow theelectronic device to send and receive data whether over a wired and/or awireless connection. In some embodiments, a physical NI may compriseradio circuitry capable of receiving data from other electronic devicesover a wireless connection and/or sending data out to other devices viaa wireless connection. This radio circuitry may include transmitter(s),receiver(s), and/or transceiver(s) suitable for radiofrequencycommunication. The radio circuitry may convert digital data into a radiosignal having the appropriate parameters (e.g., frequency, timing,channel, bandwidth, etc.). The radio signal may then be transmitted viaantennas to the appropriate recipient(s). In some embodiments, the setof physical NI(s) may comprise network interface controller(s) (NICs),also known as a network interface card, network adapter, or local areanetwork (LAN) adapter. The NIC(s) may facilitate in connecting theelectronic device to other electronic devices allowing them tocommunicate via wire through plugging in a cable to a physical portconnected to a NIC. One or more parts of an embodiment of the inventionmay be implemented using different combinations of software, firmware,and/or hardware.

A network device (ND) is an electronic device that communicativelyinterconnects other electronic devices on the network (e.g., othernetwork devices, end-user devices). Some network devices are “multipleservices network devices” that provide support for multiple networkingfunctions (e.g., routing, bridging, switching, Layer 2 aggregation,session border control, Quality of Service, and/or subscribermanagement), and/or provide support for multiple application services(e.g., data, voice, and video).

Overview

Autonomous vehicles and other types of vehicles with communicationequipment on board rely on mobile communication systems (e.g., 4G or 5Gmobile communication networks) to be capable of handling extensivelevels of data transmission with a certain Quality of Service (QoS)between the vehicle and the computing resources accessible via themobile communication networks. Such vehicles are referred to herein as‘connected vehicles’ that rely on external data input to make navigationand other decisions. Each connected vehicle operator or manufacturer mayhave different requirements depending on the design of their connectedvehicles and/or the operational and regulatory environment that theconnected vehicles operate within.

The QoS of mobile communication networks varies with time and location.The variations can be large or small and can occur within a short periodof time or within a short distance. In some cases, these variations aredriven by known patterns like road traffic and therefore are seasonaland easily predictable. But in some cases, these variations are noteasily predicted and can bring significant risk to the capability ofconnected vehicles and their operators to meet key operational andregulatory demands. Some situations that can bring up these unexpectedvariations include agglomerations of mobile communication networkservices users (e.g., concerts, traffic accidents, and similar events)or unexpected events within the mobile communication network like atower failure. Connected vehicles and their operators mitigate this riskby contracting mobile communication network service with multiple mobilenetwork operators (MNOs) with the expectation that, at any given timeand location one of the MNOs will be able to meet the QoS requirementsof the connected vehicle.

Autonomous vehicles are one example of a connected vehicle. Autonomousvehicles are discussed herein by way of example and not limitation. Oneskilled in the art would understand that the principles, functions,processes, and structures discussed herein with relation to autonomousvehicles are applicable to other types of connected vehicles and thatautonomous vehicles are referenced for sake of clarity and conciseness.The processes of the prediction system are applicable to other types ofconnected vehicles that are not necessarily autonomous, such as vehiclesthat provide assistance to drivers or pilots via any type of feedback orinformation system, or vehicles or applications that consume or providemedia or other data sources in real time and while on the move (e.g.,trucks for television networks, ambulances with connected mobileequipment, public transportation with localized services like WiFi).

The QoS provided by mobile communication networks can vary acrossdifferent areas of the mobile communication network that the connectedvehicles could traverse. The variance in QoS across the mobilecommunication network can be due to differing network capabilities indifferent portions of the network (e.g., 5G or 4G service may not beavailable in all areas of the network), due to temporary changes innetwork conditions (e.g., outages or heavy network traffic), weatherconditions affecting signal quality or similar issues.

Existing connected vehicle navigation systems focus on the shortest timeto travel when determining a route to go from point A to point B and donot take mobile communication network QoS along the route intoconsideration. In some systems, historical mobile communication networkQoS along a route is considered, where the historical mobilecommunication network QoS is collected in these systems by usersreporting the past mobile communication network QoS experienced indifferent areas on a respective mobile communication network. Thesesystems that use historical mobile communication network QoS data usecrowdsourced data and are inherently not real-time. As used herein,‘real-time’ data is data representing conditions within seconds,minutes, or hours of the data collection, rather than days, weeks, ormonths of data collection. ‘Historical’ data is collected days, weeks,or months prior to the current time. Although, these crowdsourcedhistorical data tools and/or technologies are helpful, they are renderedinapplicable or inaccurate if there is not enough data available for aspecific route (a very common problem with crowdsourced data) or ifconditions change, which can happen also frequently (e.g., from hour tohour or even from millisecond to millisecond) due to the dynamicmobility patterns of mobile communication network environments.

Connected vehicles and their operators face avoidable uncertaintieswhile choosing a route to go from Point A to Point B. The existingrouting algorithms can recommend a route that has in the past met theQoS connectivity requirements of the connected vehicle, but during thatparticular moment or in the near future when the connected vehicle isdriving along the recommended route the QoS can degrade and jeopardizethe outcome of the trip and fulfilment of its purpose. A serious QoSdegradation can endanger the public and can result in events thatnegatively impact the image and acceptance of connected vehicles.

The non-deterministic nature of the existing routing algorithms is oneof the main reasons why connected vehicle operators contract mobilecommunication services with two or more different MNOs in an attempt tobuild redundancy and reliability into the operation but, because theexisting routing algorithms do not take into account the quality of themobile communication network services in real-time, the increase in costof contracting with multiple MNOs for service does not always correlatewith a reduction of the risks of inadequate QoS and thus it is in manytimes an unnecessary cost.

The embodiments overcome these issues and deficiencies of the prior art.The embodiments provide an improved optimization method through which asystem can optimize routing options for connected vehicles with aconfidence level for the different levels of mobile communicationnetwork QoS supported by the connected vehicle and for the differentMobile Network Operators (MNOs) accessible to the connected vehicle.

The embodiments provide a route prediction method and system based onreal time mobile communication network data. The route prediction methodand system can be utilized with any connected vehicle or relatedapplication that will benefit from knowing the expected mobilecommunication network QoS for a planned trip. Based on the expectedstart time of the trip, a recommendation of which MNO to opt for on eachsegment of the route can be determined by the prediction system andprovided to a requesting connected vehicle. Thus, selecting the bestavailable MNO will ensure uninterrupted and required QoS for theconnected vehicle.

Example applications include autonomous vehicles capable of conditionaldriving automation (level 3) or above (as defined by the Society ofAutomotive Engineers in revision 2018-06 of their publication SAE J3016and subsequent revisions), unmanned aircraft (e.g., large unmannedaerial vehicles (UAVs) or smaller drones), vehicles or applications thatconsume media or other data sources in real time and while on the move(e.g., video games, streaming video applications, and similar software).For the sake of clarity and conciseness, the example of an autonomousvehicle system, referred to as an autonomous vehicle, is used herein byway of example and not limitation. One skilled in the art wouldappreciate that the principles, structures, and processes described withrelation to an autonomous vehicle are applicable to these cases andother similar connected vehicle cases.

The embodiments of the prediction system and method provide a process topredict the mobile communication system QoS that a connected vehiclewill experience along the multiple segments that make up a route fromPoint A to Point B across multiple MNOs using real time mobilecommunication network data as the primary source of data. The predictionsystem and method replace the need to have software onboard theconnected vehicles to collect training data by identifying movingconnected vehicles from real time cellular network data. Individualconnected vehicles and their experience information can be identified,and the information can be collected from mobile communication networkdata, rather than requiring the connected vehicle to directly report theexperience information. The prediction system and method increase therobustness of the route prediction process relative to using sparsecrowdsourced data. The prediction system and method identify andhighlight intermittent service degradations like coverage holes orabnormal QoS reductions using radio coverage patterns andtelecommunication domain-knowledge expertise.

A connected vehicle that is designed to utilize the embodiments can beconfigured with a profile that identifies the information that will beutilized by the connected vehicle to communicate with mobilecommunication networks and as part of the routing process. The profilecan include identifying information for the mobile communication networkoperators (MNOs) contracted with the connected vehicle and the order ofpreference for using the MNOs. The configuration information can furtherinclude the supported mobile communication network service QoS levels(e.g., at least 10 Mbps or at least 5 Mbps of uplink or downlinkthroughput), which then get mapped into Service Level Indicators (SLIs)(e.g., SLI3 for more than 10 Mbps, SLI2 for more than 5 Mbps but lessthan 10 Mbps, and SLI1 for less than 5 Mbps).

The embodiments include a prediction system and process provided by oneor more electronic devices of an operator of the prediction system andin some embodiments one or more of the MNOs. The prediction servicereceives from a connected vehicle either a request for a route for atrip or a request for a prediction for a given route for a trip. Theprofile information is also shared with the prediction system. The routeor the prediction for the given route is calculated based on the MNOsstored in the profile and using various mobile communication networkparameters and real time mobile communication network data such ascoverage area, required QoS, current subscriber level and cell levelcellular wireless QoS, antenna, alarms, past performance, handoversettings and a reliable propagation model that uses a digital model(e.g., Digital Terrain Model (DTM), Digital Elevation Model (DEM), orDigital Surface Model (DSM)) and/or clutter maps that are utilized inoptimizing and developing a realistic prediction algorithm. Thecalculations of the prediction system also consider past predictions andthe correlation of these past predictions with the actual mobilecommunication network QoS experienced by connected vehicles duringcurrent trips, or after trips are completed. The prediction service canalso use input from external sources like mapping companies, weatherservices, and similar sources.

The prediction system can receive a destination and determine a route tothat destination or can receive a set of routes from a connected vehicleto analyze. A ‘set,’ as used herein can refer to any whole number ofitems including one item. The route is split into multiple segments anda prediction is created for each segment and for each applicable MNObased on the profile for the connected vehicle. The prediction systemdetermines the most likely QoS level to be experienced in each segmentfor each MNO along the route based on the supported levels defined inthe profile. Based on the policies set by the profile or the predictionsystem, the prediction process will consider the preferred MNOs,previous segment MNO recommendation, and the most desirable keyperformance indicator (KPI) level used for prediction.

In some embodiments, the connected vehicle can include software specificto interacting with the prediction system to report the actual QoS for autilized mobile communication network experienced along the triptogether with some other relevant parameters like location, speed,driving conditions, and similar information.

The embodiments provide advantages over the existing art by the use ofreal time mobile communication network data for route prediction ofconnected vehicles. Since resource allocation is critical for supportingmobility and providing QoS in mobile communication networks, it is veryimportant to capture the movement pattern of connected vehiclesaccurately. The embodiments further provide a propagation model forpredicting issues with QoS in a mobile communication network. Predictionof path loss or degradation is an important element of the operation ofthe processes and system of the embodiments as the environment isconstantly changing with time. In some embodiments, a combination ofmachine learning models that consider weather and time aspects areutilized.

A further aspect of the embodiments is a process and system forpurchasing data from the available MNOs to provide to the connectedvehicle a single point of purchase for navigation and related QoSregardless of the number of MNOs that the connected vehicle can accessor that provide contracted service for the connected vehicle.

In some embodiments, the prediction process can be deployed (e.g., in adistributed manner) within the components and devices of a predictionsystem operator. In other embodiments, some components can be deployedwithin devices of each MNO to ensure that the proprietary data of therespective MNO that is used for the prediction process do not leave theMNO's components, devices, and facilities (i.e., only anonymized,transformed, or similarly obfuscated data is provided outside of themobile communication network of the associated MNO). However, thecomponents deployed within the devices of the MNO can be administered bythe prediction system operator.

These aspects of the embodiments can be combined in any combination orseparately to enable the finding of the best route based on forecastingmodels built on collected data including real time data of the availablemobile communication networks. The prediction model and process collect,analyze, and act upon mobile communication network data in real-time andleverage machine learning tools to generate real-time connected vehicleroute recommendations.

FIG. 1 is a diagram of one embodiment of an overview of the predictionsystem. The diagram illustrates at a high-level view the end to end dataflow of the embodiment across the prediction system. The diagramillustrates two inter-related data flows. One data flow is the onlineprocess flow and the other data flow is the offline process flow. Thediagram illustrates the interrelationship of the components as part ofthe online and offline processes, while the arrows indicate the movementof data between the component for the online and offline processes.

The online data flow process is initiated whenever there is datareceived by the prediction system from a set of Autonomous DrivingFleets (ADF) 101 via a cloud platform (CP) 102 or similar computingenvironment hosting the prediction system. The ADF is a set ofautonomous vehicles having any number or variety of autonomous vehicles.Each ADF can represent a separate service, navigation system, autonomousvehicle manufacturer or similar grouping of vehicles. The autonomousvehicles of each ADF can communicate directly with the predictionsystem, or an ADF can have supporting network infrastructure such asmobile communication networks provided by MNOs that relays theinformation to the prediction system. In some embodiments, the ADFs 101are groupings of autonomous vehicles according to their primaryconnecting MNO. The example case of ADFs 101 utilizing the predictionsystem is provided by way of example and not limitation. One skilled inthe art would understand that the prediction system can be utilized bygroups of connected vehicles in a similar manner.

The received data from the ADF 101 can include route predictionrequests. In some embodiments the received data can also include anyamount or variety of collected sensor, environment, and driving relateddata. The data received from each ADF 101 can be sent by the cloudplatform 102 to a route prediction block (RPB) 104. An RPB 104 can beexecuted on hardware of a prediction system service provider. In otherembodiments, an RPB 104 can be executed at each MNO for processing datacollected by that MNO and to get the predictions. However, even when theRPB 104 is executed on hardware of an MNO it may be administered by theprediction system service provider. An RPB 104 can provide routepredictions to a requesting ADF 101 or similar entity. Each RPB 104responds with route QoS predictions, which are then sent to (or toward,via the cloud platform 102) the ADF 101 or the specific autonomousvehicle, which requested the route prediction. The RPBs 104 can beexecuted at the same cloud platform 102, at separate computingfacilities or otherwise distributed. Each RPB 104 can include its ownpropagation model. RPBs 104 and correlation clusters (CCs) 105 can behosted on MNO network hardware (i.e., on a per MNO basis). In otherembodiments, the RPBs 104 or CCs 105 can be hosted on hardware of aprediction system service provider. In either case, the predictionsystem service provider can administer the RPBs 104.

RPB 104 includes a machine learning forecasting model, radio propagationmodel referred to herein as a ‘propagation model,’ and focused mobilitymodel. The machine learning forecasting model estimates and predicts thekey performance indicators (KPIs) for each vehicle and route segment bytaking engineered telecom features as inputs. Mobile communicationnetwork features include network KPIs and subscriber KPIs filtered bythe focused mobility model. The focused mobility model is described withrelation to FIGS. 5 and 6 herein.

The online process can be further divided into an online scoring processand online experience correction process based on the data received fromthe ADF 101. The online scoring process is initiated whenever a requestis received from the ADF 101 with a list of locations, segments, orsimilar identifiers for which a QoS score or rating is requested. Theprediction system as implemented via the respective RPB 104 responds tothe ADF 101 by sending the predicted QoS scoring of the given identifiedlocations, segments, or similar elements identified by the request fromthe ADF 101. Multiple online processes could be running at the same timeand each of the online process would be tracked by a process or requestID to enable the received requests to be correlated with responses fromthe associated RPB 104.

An online experience correction process can be initiated whenever an ADF101 experience data is received by the RPB 104. This experience data iscorrelated with the past predictions of the online process at RPB 104.Thus, the predicted QoS provided to the ADF 101 by the RPB 104 iscorrelated with the experience data returned by the ADF 101 to comparethe prediction with the actual experienced QoS. This correlation ofexperience data enables the measuring of the existing accuracy of themodels along with providing a mechanism for correction for the futurepredictions. The online experience correction captures information thatthe ADF 101 may share with the CP 102, e.g., either when making theroute prediction request or at a later time, and uses this informationto identify specific deviations and anomalies of a given ADF 101 andaccount for them in the offline models.

The offline process is a continuous process that runs at all times or atregular intervals. The offline process collects and correlates the datafrom the mobile communication network 106 and experience data from theADF 101. The collected data is enhanced with other sources 103 and pastcollected data. This correlated data is stored for historical purposesby the associated RPB 104. The data collected from the mobilecommunication network 106 can include QoS information such as throughputmetrics, operational status, latency and similar information. In someembodiments, each MNO can communicate the mobile communication networkdata to correlation clusters (CC) 105. The CC can be administered by anMNO or by a separate provider operating the prediction system. The CCcan aggregate information for the mobile communication networkassociated with the MNO or similarly organizes or marshals the data tobe provided to the RPB 104 associated with the MNO.

The offline process can be further divided into an offline service levelindicator (SLI) modelling process and an offline retraining process.Multiple offline models can be created from combinations of thesesources. The offline models can include an SLI model, mobility model,and/or a propagation model, which are referred to herein as offlinemodels, generally. The SLI model matches network information such ascell tower KPI values to an SLI value in a geographical area relevant toa route or segment. The SLI modelling involves the network data beingfed to all the offline models, which are then scored based onperformance. Any number and variety of offline models can be utilized byeach RPB 104. Each offline model can be a machine learning (ML) modelhaving been trained on different sets of input data to generate SLIpredictions for areas, segments, routes, locations, or similar divisionsof a mobile communication network.

A mobility model identifies moving/driving devices from the mobilecommunication network data to target more relevant data for offlinetraining. A propagation model manages how a geographical area is coveredby a mobile communication network (e.g., specific cell tower) anddetermines KPI values for more precise geolocation. In some embodiments,a routing model is utilized to split a route into segments and canoptimize a route as well as match forecast SLI levels from the SLImodel. The routing model is considered to be part of the online process.Different offline models can use different inputs and training sets. Forexample, an offline model can include input information based on theweather and time of the day that can be utilized to contribute to theoverall route prediction. The weather influenced offline models takeinto consideration atmospheric affects, which contribute on average to−3 to −20 dB loss for signals in a mobile communication network. Thebest performing offline model in the offline process is promoted to makepredictions in the online process. Similarly, the current offline modelcan be downgraded from use by the online process.

The offline retraining process involves storing the experience datareceived from the ADF 101. The experience data is then compared withactual forecast data for the offline models and the deviation betweenthe actual forecast data and experience data is used to correct theoffline models and fed to the offline models as training data oradjustments to hyperparameters, which enables the offline models tocontinuously learn from the feedback experience data. The offline modelretraining process extracts the past experiences of the ADF 101 from themobile communication network data and uses this information to correctthe offline models.

In the diagram of FIG. 1 , the offline process shows the ADF 101providing vehicle experience feedback about the constituent autonomousvehicles and their requests for predictions. The vehicle experiencefeedback can include location information, actual QoS information, andsimilar information. In some embodiments, the vehicle experienceinformation is forwarded from the CP 102 to the RPBs 104 or othercomponents.

The ADF 101 receives prediction information from the CP 102 as part ofthe online process in response to prediction requests. In someembodiments, the ADF 101 further provides vehicle experience feedbackinformation to the CP 102 with the prediction requests or separately.The CP 102 aggregates the location information into latitude longitudepair (LLP) list information that lists requested locations where aprediction is requested. The LLP list is sent to the RPB 104. The RPB104 generates a set of SLI predictions based on the current propagationmodel that are returned to the CP 102. The CP 102 then forwardsexperience data to the RPB 104. The RPB 104 also receives continuousupdates of mobile communication network metrics from a correlationcluster 105 that is collected from the mobile communication network 106.

The operation of the components in the online and offline process arefurther described in relation to FIGS. 2 and 3 , which further breakdown the operation of each of the components in the prediction system.

These components are independent from each other and can be consideredas autonomous and substitutable modules. Such a characteristic of theoverall process allows the prediction system to isolate computationallyheavy data correlation and modeling steps from lightweight applicationprogramming interface (API) calls from a potential end-user (e.g., froman autonomous vehicle). Additionally, the prediction system gives theadvantage of connecting the models of the online process with differentoffline modules by configuration depending on the preferences of theadministrators.

The operations in the flow diagrams will be described with reference tothe exemplary embodiments of the other figures. However, it should beunderstood that the operations of the flow diagrams can be performed byembodiments of the invention other than those discussed with referenceto the other figures, and the embodiments of the invention discussedwith reference to these other figures can perform operations differentthan those discussed with reference to the flow diagrams.

FIG. 2A is a diagram of one embodiment of the online process of theprediction system. The illustrated example shows the API call flow fromthe prediction request of the autonomous driving vehicle (ADV) 201 tothe end response returned by the CP 202 to the ADV 201. The onlineprocess uses the best current offline model available to the RPB 203,which will be used for scoring the ADV input data. The process startswith the ADV system 201 sending a prediction request (e.g., using a POSTrequest) with detailed route information including Latitude LongitudePairs (LLP) and other supporting information 205 to the API deployed onthe CP 202. The posting of route and experience information 205 can beon a per ADV 201 basis with individual data points or can be anaggregation of information (bulk). The route and experience data will bepre-processed and cleaned for further use 206 by the CP 202 with themost relevant information sent to the RPB 207 (e.g., via a push routeinformation). The pre-processing can include creating several possibleroutes if the request by the ADV 201 does not include a pre-definedroute. Any process or mechanism can be used to clean or process the dataat the CP 202 such as discarding of malformed data, removal of duplicatedata, and similar processes.

The RPB 203 can store or access mobile communication network topologyand operation data (e.g., cell tower network data and similaroperational metrics) and correlates this information with the route LLP.For example, the mobile communication network topology and operationdata can be correlated with the route and experience data requestsaccording to the distance of mobile communication equipment (e.g., celltowers) from the route and cell tower characteristics. The RPB 203 canproceed with correlating detailed location information (e.g., relevantcell towers or grids) provided for the forecasted Key PerformanceIndicator (KPI) value selection with best offline model for each grid.In some embodiments, the offline models are targeted to cell coveragearea (e.g., with a focus on the cell tower) or geographical grids (e.g.,a focus on latitude and longitude rectangles). With the grid approach,when there is a forecast, the relevant route area is split into smallgrids (e.g., 10×10 or 30×30 meters) and an experience forecast is madefor those grids regardless of cell locations, which can be taken care bypropagation modeling. In other embodiments, there is a forecast made foreach cell, that is related to its coverage zone. An approximateprobability distribution is made for an area being served by each of thecell towers. The experience forecast combines these probabilitydistributions as a weighted average.

This correlation process is followed by an online scoring process usingthe best offline model, adjusted with the current ADV information,aggregated into SLI level predictions based on dynamic threshold levels,and then correlated to locations of route segments 208. Finally,forecasted SLI levels for route segments are sent back to the cloudplatform 209 and then the CP 202 sends the SLI levels to the initialcall sender ADV 210. While the forecasting of SLI levels is donecontinuously in short-term batch mode, the KPI value forecasts arelinked to the route not only by location from a given LLP, but alsolinked by the time taking into consideration either expected vehiclespeed and distance (e.g., in a default mode) or third-party routing APIpredictions (e.g., in an advanced mode).

FIG. 2B is a flowchart of one embodiment of the online processimplemented by the RPB. The diagram is described as an operation of asingle RPB associated with one MNO, however, the process applies toembodiments where multiple RPBs are operating in association withmultiple MNOs. The RPB process is initiated by receiving a route requestthat originated with an ADV (Block 251). The route request can specify aroute using an LLP list or similar description of the route. In someembodiments, experience data indicating actual QoS for a given locationand MNO can be provided. The RPB can organize the route information intosegments (Block 253). Segments of the route can correspond to areascovered by the mobile communication network or be similarly defined;thus, the mobile communication network resources are correlated with theroute and specifically with particular segments of the route (Block255).

A set of KPI levels is identified for a segment as determined by thecurrent offline model utilized by the online process (Block 257). Eachoffline model can utilize different inputs. For example, some offlinemodels utilize specific weather condition information (e.g., cloudcoverage or precipitation), while others may not. The current offlinemodel, which is an offline model selected for use by the online process,outputs a set of KPIs that rate mobile communication network coveragefor a segment (e.g., for a particular cell tower that covers a segment).Where current experience data has been received, this data can beprocessed, which can be utilized to adjust the KPIs of the mobilecommunication network or specific components of the mobile communicationnetwork (Block 259). For example, if the ADV reports low QoS for acurrent segment and selected mobile communication network, then thatrating (i.e., KPIs) can be downgraded or similarly adjusted to reflectthe feedback. The set of output KPIs from the current offline model canbe aggregated and correlated with SLI levels (Block 261). The KPIsoutput by the current offline model can be correlated with SLI levels byusing threshold levels or similar mechanism for mapping the KPIs withSLI levels.

The process can then aggregate the SLI levels for the mobilecommunication network components and resources for each segment of thereceived route (Block 263). This set of SLI levels or similar QoSratings for each segment can then be prepared and sent to (or toward,via the cloud platform 202) the requester using any communicationmethod, protocol, or format (Block 265).

FIG. 3A is a diagram of one embodiment of an offline process for theprediction system. The example diagram of FIG. 3A illustrates theoffline process for a per network resource (e.g., cell tower) KPI levelforecast generation. The offline process can be initiated with rawnetwork data correlation 305 in the scalable correlation cluster (CC)304. Processed data (e.g., KPI values for relevant cells) is thencontinuously pushed 306 to the specific RPB 303 where the data isaccumulated for predictive modeling. Once enough data is obtained, a setof offline models are built, e.g., one or more models targeting per celltower KPI values prediction 307. The building of the offline modelsutilizes machine learning techniques. The received network data isaccumulated with historical data. The data can be filtered to focus ondata relevant to servicing mobile devices (i.e., autonomous vehiclesincluding ADVs). Each of the offline models can be trained using theaccumulated data or varying subsets thereof.

In one example embodiment, each cell tower Time Series (TS) data will beclassified in terms of predictability and statistical characteristics.The updated offline models are validated to confirm that they performappropriately with test input or similar validation mechanism. Theupdated offline models can be model objects and along with theirvalidations the model objects are stored for use by the online processwhere the online process can select to use the offline model that hasthe best accuracy, experience data feedback, or similar selectionmechanism. Periodically, a table or similar data structure tracking theoffline models and validation results is updated at the CP with a pushjob 308 or similar update mechanism from the RPB to keep most up-to-dateinformation available in the CP to the ADVs.

FIG. 3B is a flowchart of one embodiment of the operation of the RPBimplementing the offline process. The RPB can initiate an iteration ofthe offline process to update the offline models in response toreceiving network data from a CC or similar source of mobilecommunication network data (Block 351). The network data can include anynumber of different metrics for any number of mobile communicationnetwork devices including operational status, throughput, latency,maintenance, resource utilization, and similar information. The receivedmobile communication network data can be combined with previouslyreceived mobile communication network data to form a training data setto train the offline models (Block 353). This training data set can befiltered or similarly refined to focus on mobile communication networkdata that is relevant to predicting QoS for autonomous vehicles as theytraverse a route (Block 355).

With the training data set prepared, the offline models can be trainedagainst the updated information (Block 357). After the offline modelshave been trained and further modified, they may be validated by testingmechanisms to determine the accuracy of each offline model and identifyerrors (Block 361). The offline model with the best performance can beselected for use in the online process and can be referred to as the‘current’ offline model while in use by the online process (Block 363).

FIG. 4 is a diagram of one example of a propagation model. The examplepropagation model with different combinations for the time of the dayand weather is generated by providing a Digital Terrain Model (DTM),Cluster Classes, and antenna properties 401. In other embodiments, aDigital Elevation Model (DEM) or Digital Surface Model (DSM) may beutilized instead of, or in addition to, a DTM. In the illustratedexample, the propagation model is composed of sixteen different modelsthat are generated for different time-weather combinations 402. Thepropagation model can output information such as KPIs including ReceivedSignal Received Power (RSRP) and/or Received Signal Received Quality(RSRQ) 403. Multiple propagation models can be maintained and theproperties of each can vary according to the MNO and similarconsiderations. The predictions of the propagation model are fed to theRPB (e.g., an autonomous vehicle router (AVR)) 404.

FIG. 5 is a flowchart of one example embodiment of a process offiltering the network data to be used by the offline process. Thefiltering process can be implemented by a focused mobility model in theoffline process. The example filtering process is provided by way ofexample and not limitation, and one skilled in the art would understandthat other similar filtering processes can be utilized in conjunctionwith the online and offline processes. The process provides aclassification of the data to filter out the data that is not relevantfor training offline models. The filtering process filters network datato identify network data associated with high mobility. The input datais obtained from a CC (Block 501) and can be filtered sequentially toget desired data for the offline process. In the example, the firstfilter validates whether within a short-continuous time flow anyhandovers have occurred (Block 502). If the data does not relate tohandovers, then this data can be discarded or ignored. For data that isrelated to handovers, the function checks whether the distance betweenmobile communication network cells is not walkable (i.e., a longdistance) for a given time period (Block 503). If the data is related toa walkable or short distance, then the data can be discarded or ignored.For data that is related to a longer distance (i.e., a non-walkabledistance), the data is analyzed to determine whether it correlatesgeo-spatially with nearby roads (Block 504) to confirm the data isrelevant to moving along public roads that may be navigated by theautonomous vehicle. If the data is not correlated with a navigable road,then it may be discarded or ignored. The data that is related tonavigable roads can be stored for using in the offline process (Block505). This filter function is provided by way of example and notlimitation. The filtering of data can utilize any number or variety ofclassifications that can improve the relevance of the data to the routeprediction process of the prediction system.

FIG. 6 is a flowchart of one embodiment of a process of a mappingfunction to correlate mobile communication network data to routes. Theexample mapping function is provided by way of example and notlimitation, and one skilled in the art would understand that othersimilar mapping functions can be utilized in conjunction with the onlineand offline processes. In the illustrated example, the mapping functionidentifies relevant mobile communication network components (e.g., celltowers) for an input route. The process can be initiated by an API callof the CP that provides an LLP list for a route (Block 601). The mappingfunction also utilizes as input mobile communication network componentLLP information (e.g., cell tower location information) from a referencedata set (Block 602). These data sets are merged for further processing(Block 603).

There are two groups of correlations involved in the mapping process,whether mobile communication network components (e.g., cells) arelocated geographically close enough to the input route (Block 604) andwhether mobile communication network components (e.g., cells) havephysically strong signal coverage on the input route (Block 605)considering cell characteristics such as frequency band, tilt angle,probability of being selected in that area, and similar consideration.If mobile communication network components are not geo-relevant or donot provide route coverage, then the data associated with thesecomponents can be ignored or discarded. Based on this mapping onlyrelevant mobile communication network components (e.g., tower cells) areoutput for further data filtering or processing in the offline process(Block 606).

FIG. 7 is a flowchart of one embodiment of the offline SLI modelingimplementation. The illustrated process illustrates offline processapplication where a best (i.e., winning forecasting model) is selectedto be applied for the online process. The example forecasting modeltraining is provided by way of example and not limitation, and oneskilled in the art would understand that other similar modelingfunctions can be utilized in conjunction with the online and offlineprocesses. The process is initiated using input mobile communicationnetwork data that is obtained from a CC or similar source (Block 701).The input data is filtered for relevance for routing with autonomousvehicles (e.g., as set forth with relation to the filtering functiondescribed herein) and classified into different time series (TS)clusters according to series statistical characteristics (Block 702). Anoffline model corresponding to the most probable class is trained on TSdata (Block 703) which can then be analyzed with cross-validationtechniques (Block 704). All offline models, validation metrics, and thebest model object will be stored (Block 705) for further use in theonline process.

FIG. 8 is a flowchart of one example embodiment of feedback function forthe online SLI scoring implementation. The example feedback function isprovided by way of example and not limitation, and one skilled in theart would understand that other similar feedback functions can beutilized in conjunction with the online and offline processes. Asdescribed herein, the online scoring process utilizes an offline modelidentified by the offline process for generating route predictioninformation. The feedback function utilizes experience data reportedfrom autonomous vehicles to determine the accuracy of the predictionsmade by the prediction service. In this example embodiment, the feedbackfunction of the online scoring process can be initiated in response toreceiving data obtained from the ADF (e.g., through an API POST call)(Block 801) or similar mechanism. The feedback function accesses thecurrently selected offline model as identified by the offline process(Block 802). A check is made whether the API POST call includesexperience data (Block 803). In the case where the API POST call doesnot contain experience data or similar information about current networkperformance and mobility experience, the current offline model will beused to score the minimal required data and the process is completed. Insome embodiments, the received experience data from the API POST callwill be used with additional indicators of current performance (Block804) by correlating current experience data (i.e., indirect KPIs) withpredicted main KPI values (Block 805). If the predicted values aremisaligned with the current performance, then the forecast will beadjusted by the misalignment factor (Block 806).

FIG. 9 is a flowchart of one example embodiment of an offline retrainingprocess. The example retraining function is provided by way of exampleand not limitation, and one skilled in the art would understand thatother similar retraining functions can be utilized in conjunction withthe online and offline processes. The retraining function enriches theoffline modeling with relevant validation responses. The retrainingprocess can be initiated in response to receiving current drivingexperience data from an ADF (e.g., through API call of the CP) (Block901). The experience data is stored by and accessible to the RPB forback testing and historical comparison with main KPI's dynamics (Block902). The received experience data indicates actual QoS experience thatis compared with the predicted QoS from the offline model. The QoSexperience data and the predicted QoS data can be expressed as SLIlevels, KPIs, and similar metrics on a per segment, per route, perlocation, per mobile communication network component or similar basis.Based on the comparison, a deviation between actual values and predictedindirect indicators (e.g., SLI levels and KPI values) is calculated(Block 903). A check is made of the deviation to determine whether thereis a large bias (Block 904), wherein if the bias is large thenobservations receive a weight increase according to the deviation values(Block 905) and full historical data with adjusted observation weightscan be rebuilt by the corresponding offline process at regular intervals(e.g., daily) (Block 906). If a large bias is not detected, then theweighting may not be adjusted and the offline process can rebuild theoffline model at regular intervals (e.g., daily).

FIG. 10 is a diagram of one embodiment of a system for brokeringinformation between multiple MNO sources. The illustrated brokeringsystem demonstrates a high-level view of the data sources and thebrokering process. The functions of the brokering process are furtherdescribed with relation to FIGS. 11-13 . In particular, the functiondescribed with relation to FIGS. 11-13 illustrate how the predictionsystem and services can be distributed such that proprietary informationof each MNO is protected and thereby enable the brokering of the neededinformation that is to be shared by the MNOs with the prediction systemand services.

In FIG. 10 , the ADF 1001 receive prediction information (e.g.,predicted QoS information for segments) 1005 in response to queries tothe prediction system for a specified route. As a result of theprediction system processes there is a monetary accounting flow betweenthe requesting ADFs 1001 and the prediction system and predictionservices. The prediction information can be provided via the CP 1002using multiple data sources. The primary data source (e.g., mobilecommunication network metric information) can be the MNOs which providetheir live data and help maintain the ecosystem for the RPB 1004 locatedwith or associated with the respective MNO. The RPBs 1004 can be aservice provided by the prediction system and managed by an operator ofthe prediction service. This creates a monetary accounting flow betweenthe prediction system and service at the CP and the RPBs at therespective MNOs based on the number of requests for mobile communicationnetwork data sent to the respective MNO. There can also be otherthird-party data sources 1003, which provide weather, traffic, bestroutes, DTM, and similar information for the prediction system andservices, wherein this relationship creates a monetary accounting flowbetween the prediction system and prediction service and each of thethird party sources depending on the number of requests for therespective third-party services. In one example embodiment, theprediction system and service are implemented on an Open Cloud Platform1002 or similar platform, which creates a monetary accounting flowbetween the prediction system and service and the CP provider. This setof monetary accounting relationships is handled by a set of brokeringfunctions that handle the accounting of charges between theinter-related components and systems.

FIG. 11 is a flowchart of one embodiment of the process of integratingan ADF with the prediction system. The process includes the tracking ofinformation relevant to each ADF and the autonomous vehicles (e.g., ADV)that make up the ADF. The process can be implemented by the predictionsystem and services at the CP and can be initiated by receiving ADFspecific parameters to create ADF specific business logic (Block 1101).The received parameters are stored in an ADF database 1105 (Block 1103).The ADF database 1105 can include any number of records 1107 thatprovide parameters relevant to each ADF such as country of operation,KPI to SLI level mappings, KPI thresholds, subscription information andsimilar parameters.

FIG. 12 is a flowchart of one embodiment of the online processimplemented at a CP to support information brokering. The processhandles prediction requests at the CP for the prediction system andservices. The example prediction request handling function is providedby way of example and not limitation, and one skilled in the art wouldunderstand that other similar prediction request handling functions canbe utilized in conjunction with the online scoring process. Theprediction request handling process can be initiated in response toreceiving a ‘push’ prediction request or similar prediction request froman autonomous vehicle (e.g., ADV) of an associated ADF (Block 1201). Acheck is made whether the source of the route is to be determined by athird-party data source or provided by the autonomous vehicle (Block1203). Where the route is to be sourced from a third-party or other datasource (e.g., the autonomous vehicle provides only a destination andstarting location that requires routing at the CP or external service),then the route is determined based on the available data from therequest using a CP service or external service (Block 1205).

Where the autonomous vehicle provides a route or after it is determined,the route is split into segments (Block 1207). Any process or functioncan be used to identify segments including correlation with mobilecommunication network component locations, coverage maps, or similartechniques. Available MNOs (e.g., local mobile communication networks)for the country of origin for the autonomous vehicle and in some casespreferred providers can be determined using parameters stored in the ADFdatabase (Block 1209). For example, the ADF database can be queried forthe MNOs a given ADF or autonomous vehicle utilizes in a given country.Route information including segment identification can then be forwarded(‘pushed’) to each of the RPBs of respective MNOs that are identifiedbased on an ordered list of MNOs (Block 1211).

FIG. 13 is a flowchart of one example embodiment of a process forhandling route information at a CP for the prediction system and serviceto support information brokering. The example push route function isprovided by way of example and not limitation, and one skilled in theart would understand that other similar push route functions can beutilized in conjunction with the online scoring processes. The pushroute process can be initiated where a prediction request is received bydetermining whether MNO bonding has occurred (i.e., whether an ADV usesMNO bonding) (Block 1301). An ADV using MNO bonding will access multipleMNOs simultaneously, while an ADV not using MNO bonding will use asingle MNO at a time although it may switch between MNOs depending onthe predictions provided by the prediction system. Where there is no MNObonding, the push route process sends route information to the RPB ofthe first MNO in the ordered list (Block 1303). A check is made whetherall segments have a first KPI value above a designated threshold (e.g.,as defined in the ADF parameters) (Block 1305). If all of the segmentsdo not have a first KPI value above the threshold, then the routeinformation is sent to the next MNO in the ordered list (Block 1307).Similar checks are made of additional KPI values (Block 1309-1311),until it is found that all of the KPI values are above the respectivethreshold or the route information is sent to the RPB of the next MNO inthe ordered list.

If all segments of the route have KPI values above the respectivethresholds, then the process combines the predicted SLIs for the routefrom the RPBs that received the route information (Block 1317). Thecombined predicted SLIs are then sent (‘pushed’) to the requesting ADV(Block 1319). Similarly, if there is MNO bonding (Block 1301), then apush of the route information to the RPB of each MNO in the ordered listis performed (Block 1315). The process then combines the predicted SLIsfor the route from the RPBs that received the route information (Block1317). The combined predicted SLIs are then sent (pushed') to therequesting ADV (Block 1319).

FIG. 14 is a diagram of one embodiment of a cloud implementation of theprediction system and service. FIG. 14 describes the flow of onlinescoring processing in the cloud implementation. The embodiments utilizea cloud platform to receive and send data from ADVs 1401, 1405. Theembodiments are containerized and partially sit on the cloud platformmaking it easily scalable leading to a distributed system. Moreover, theother components of the prediction system and services can run in adistributed manner (including all the offline modeling, online andoffline processes and similar components) on the RPB 1402, 1403. Theentire end to end flow can use rest API's which leads to a system whichis distributed and scalable not only vertically but horizontally aswell. The data flow from ADV 1401 is sent to the RPB using APIs 1402,which are then worked upon and predictions added and sent back from theRPB 1403. Since feedback is handled in a loop, the predictions arereceived at the ADV, which in turn provides experience data. Theexperience data is used to grade predictions 1404 that are sent out tothe corresponding ADV 1405, closing the data loop.

FIG. 15A illustrates connectivity between network devices (NDs) withinan exemplary network, as well as three exemplary implementations of theNDs, according to some embodiments of the invention. FIG. 15A shows NDs1500A-H, and their connectivity by way of lines between 1500A-1500B,1500B-1500C, 1500C-1500D, 1500D-1500E, 1500E-1500F, 1500F-1500G, and1500A-1500G, as well as between 1500H and each of 1500A, 1500C, 1500D,and 1500G. These NDs are physical devices, and the connectivity betweenthese NDs can be wireless or wired (often referred to as a link). Anadditional line extending from NDs 1500A, 1500E, and 1500F illustratesthat these NDs act as ingress and egress points for the network (andthus, these NDs are sometimes referred to as edge NDs; while the otherNDs may be called core NDs).

Two of the exemplary ND implementations in FIG. 15A are: 1) aspecial-purpose network device 1502 that uses customapplication—specific integrated—circuits (ASICs) and a special-purposeoperating system (OS); and 2) a general purpose network device 1504 thatuses common off-the-shelf (COTS) processors and a standard OS.

The special-purpose network device 1502 includes networking hardware1510 comprising a set of one or more processor(s) 1512, forwardingresource(s) 1514 (which typically include one or more ASICs and/ornetwork processors), and physical network interfaces (NIs) 1516 (throughwhich network connections are made, such as those shown by theconnectivity between NDs 1500A-H), as well as non-transitory machinereadable storage media 1518 having stored therein networking software1520. During operation, the networking software 1520 may be executed bythe networking hardware 1510 to instantiate a set of one or morenetworking software instance(s) 1522. Each of the networking softwareinstance(s) 1522, and that part of the networking hardware 1510 thatexecutes that network software instance (be it hardware dedicated tothat networking software instance and/or time slices of hardwaretemporally shared by that networking software instance with others ofthe networking software instance(s) 1522), form a separate virtualnetwork element 1530A-R. Each of the virtual network element(s) (VNEs)1530A-R includes a control communication and configuration module1532A-R (sometimes referred to as a local control module or controlcommunication module) and forwarding table(s) 1534A-R, such that a givenvirtual network element (e.g., 1530A) includes the control communicationand configuration module (e.g., 1532A), a set of one or more forwardingtable(s) (e.g., 1534A), and that portion of the networking hardware 1510that executes the virtual network element (e.g., 1530A).

The non-transitory machine readable storage media 1518 can also havestored therein prediction services 1565. The prediction services 1565can include any number or combinations of functions related to theprediction system described herein. The prediction services 1565 candistributed across multiple special purpose network devices 1502 as wellas other devices.

The special-purpose network device 1502 is often physically and/orlogically considered to include: 1) a ND control plane 1524 (sometimesreferred to as a control plane) comprising the processor(s) 1512 thatexecute the control communication and configuration module(s) 1532A-R;and 2) a ND forwarding plane 1526 (sometimes referred to as a forwardingplane, a data plane, or a media plane) comprising the forwardingresource(s) 1514 that utilize the forwarding table(s) 1534A-R and thephysical NIs 1516. By way of example, where the ND is a router (or isimplementing routing functionality), the ND control plane 1524 (theprocessor(s) 1512 executing the control communication and configurationmodule(s) 1532A-R) is typically responsible for participating incontrolling how data (e.g., packets) is to be routed (e.g., the next hopfor the data and the outgoing physical NI for that data) and storingthat routing information in the forwarding table(s) 1534A-R, and the NDforwarding plane 1526 is responsible for receiving that data on thephysical NIs 1516 and forwarding that data out the appropriate ones ofthe physical NIs 1516 based on the forwarding table(s) 1534A-R.

FIG. 15B illustrates an exemplary way to implement the special-purposenetwork device 1502 according to some embodiments of the invention. FIG.15B shows a special-purpose network device including cards 1538(typically hot pluggable). While in some embodiments the cards 1538 areof two types (one or more that operate as the ND forwarding plane 1526(sometimes called line cards), and one or more that operate to implementthe ND control plane 1524 (sometimes called control cards)), alternativeembodiments may combine functionality onto a single card and/or includeadditional card types (e.g., one additional type of card is called aservice card, resource card, or multi-application card). A service cardcan provide specialized processing (e.g., Layer 4 to Layer 7 services(e.g., firewall, Internet Protocol Security (IPsec), Secure SocketsLayer (SSL)/Transport Layer Security (TLS), Intrusion Detection System(IDS), peer-to-peer (P2P), Voice over IP (VoIP) Session BorderController, Mobile Wireless Gateways (Gateway General Packet RadioService (GPRS) Support Node (GGSN), Evolved Packet Core (EPC) Gateway)).By way of example, a service card may be used to terminate IPsec tunnelsand execute the attendant authentication and encryption algorithms.These cards are coupled together through one or more interconnectmechanisms illustrated as backplane 1536 (e.g., a first full meshcoupling the line cards and a second full mesh coupling all of thecards).

Returning to FIG. 15A, the general purpose network device 1504 includeshardware 1540 comprising a set of one or more processor(s) 1542 (whichare often COTS processors) and physical NIs 1546, as well asnon-transitory machine readable storage media 1548 having stored thereinsoftware 1550. During operation, the processor(s) 1542 execute thesoftware 1550 to instantiate one or more sets of one or moreapplications 1564A-R. While one embodiment does not implementvirtualization, alternative embodiments may use different forms ofvirtualization. For example, in one such alternative embodiment thevirtualization layer 1554 represents the kernel of an operating system(or a shim executing on a base operating system) that allows for thecreation of multiple instances 1562A-R called software containers thatmay each be used to execute one (or more) of the sets of applications1564A-R; where the multiple software containers (also calledvirtualization engines, virtual private servers, or jails) are userspaces (typically a virtual memory space) that are separate from eachother and separate from the kernel space in which the operating systemis run; and where the set of applications running in a given user space,unless explicitly allowed, cannot access the memory of the otherprocesses. In another such alternative embodiment the virtualizationlayer 1554 represents a hypervisor (sometimes referred to as a virtualmachine monitor (VMM)) or a hypervisor executing on top of a hostoperating system, and each of the sets of applications 1564A-R is run ontop of a guest operating system within an instance 1562A-R called avirtual machine (which may in some cases be considered a tightlyisolated form of software container) that is run on top of thehypervisor—the guest operating system and application may not know theyare running on a virtual machine as opposed to running on a “bare metal”host electronic device, or through para-virtualization the operatingsystem and/or application may be aware of the presence of virtualizationfor optimization purposes. In yet other alternative embodiments, one,some or all of the applications are implemented as unikernel(s), whichcan be generated by compiling directly with an application only alimited set of libraries (e.g., from a library operating system (LibOS)including drivers/libraries of OS services) that provide the particularOS services needed by the application. As a unikernel can be implementedto run directly on hardware 1540, directly on a hypervisor (in whichcase the unikernel is sometimes described as running within a LibOSvirtual machine), or in a software container, embodiments can beimplemented fully with unikernels running directly on a hypervisorrepresented by virtualization layer 1554, unikernels running withinsoftware containers represented by instances 1562A-R, or as acombination of unikernels and the above-described techniques (e.g.,unikernels and virtual machines both run directly on a hypervisor,unikernels and sets of applications that are run in different softwarecontainers).

The non-transitory machine readable storage media 1548 can also havestored therein prediction services 1565. The prediction services 1565can include any number or combinations of functions related to theprediction system described herein. The prediction services 1565 candistributed across multiple general purpose network device 1504 as wellas other devices. Similarly, the prediction services 1565 can beimplemented in any number of general purpose electronic devices such asin a cloud computing environment that is in communication with a mobilecommunication network.

The instantiation of the one or more sets of one or more applications1564A-R, as well as virtualization if implemented, are collectivelyreferred to as software instance(s) 1552. Each set of applications1564A-R, corresponding virtualization construct (e.g., instance 1562A-R)if implemented, and that part of the hardware 1540 that executes them(be it hardware dedicated to that execution and/or time slices ofhardware temporally shared), forms a separate virtual network element(s)1560A-R.

The virtual network element(s) 1560A-R perform similar functionality tothe virtual network element(s) 1530A-R—e.g., similar to the controlcommunication and configuration module(s) 1532A and forwarding table(s)1534A (this virtualization of the hardware 1540 is sometimes referred toas network function virtualization (NFV)). Thus, NFV may be used toconsolidate many network equipment types onto industry standard highvolume server hardware, physical switches, and physical storage, whichcould be located in Data centers, NDs, and customer premise equipment(CPE). While embodiments of the invention are illustrated with eachinstance 1562A-R corresponding to one VNE 1560A-R, alternativeembodiments may implement this correspondence at a finer levelgranularity (e.g., line card virtual machines virtualize line cards,control card virtual machine virtualize control cards, etc.); it shouldbe understood that the techniques described herein with reference to acorrespondence of instances 1562A-R to VNEs also apply to embodimentswhere such a finer level of granularity and/or unikernels are used.

In certain embodiments, the virtualization layer 1554 includes a virtualswitch that provides similar forwarding services as a physical Ethernetswitch. Specifically, this virtual switch forwards traffic betweeninstances 1562A-R and the physical NI(s) 1546, as well as optionallybetween the instances 1562A-R; in addition, this virtual switch mayenforce network isolation between the VNEs 1560A-R that by policy arenot permitted to communicate with each other (e.g., by honoring virtuallocal area networks (VLANs)).

The third exemplary ND implementation in FIG. 15A is a hybrid networkdevice 1506, which includes both custom ASICs/special-purpose OS andCOTS processors/standard OS in a single ND or a single card within anND. In certain embodiments of such a hybrid network device, a platformVM (i.e., a VM that that implements the functionality of thespecial-purpose network device 1502) could provide forpara-virtualization to the networking hardware present in the hybridnetwork device 1506.

Regardless of the above exemplary implementations of an ND, when asingle one of multiple VNEs implemented by an ND is being considered(e.g., only one of the VNEs is part of a given virtual network) or whereonly a single VNE is currently being implemented by an ND, the shortenedterm network element (NE) is sometimes used to refer to that VNE. Alsoin all of the above exemplary implementations, each of the VNEs (e.g.,VNE(s) 1530A-R, VNEs 1560A-R, and those in the hybrid network device1506) receives data on the physical NIs (e.g., 1516, 1546) and forwardsthat data out the appropriate ones of the physical NIs (e.g., 1516,1546). For example, a VNE implementing IP router functionality forwardsIP packets on the basis of some of the IP header information in the IPpacket; where IP header information includes source IP address,destination IP address, source port, destination port (where “sourceport” and “destination port” refer herein to protocol ports, as opposedto physical ports of a ND), transport protocol (e.g., user datagramprotocol (UDP), Transmission Control Protocol (TCP), and differentiatedservices code point (DSCP) values.

FIG. 15C illustrates various exemplary ways in which VNEs may be coupledaccording to some embodiments of the invention. FIG. 15C shows VNEs1570A.1-1570A.P (and optionally VNEs 1570A.Q-1570A.R) implemented in ND1500A and VNE 1570H.1 in ND 1500H. In FIG. 15C, VNEs 1570A.1-P areseparate from each other in the sense that they can receive packets fromoutside ND 1500A and forward packets outside of ND 1500A; VNE 1570A.1 iscoupled with VNE 1570H.1, and thus they communicate packets betweentheir respective NDs; VNE 1570A.2-1570A.3 may optionally forward packetsbetween themselves without forwarding them outside of the ND 1500A; andVNE 1570A.P may optionally be the first in a chain of VNEs that includesVNE 1570A.Q followed by VNE 1570A.R (this is sometimes referred to asdynamic service chaining, where each of the VNEs in the series of VNEsprovides a different service—e.g., one or more layer 4-7 networkservices). While FIG. 15C illustrates various exemplary relationshipsbetween the VNEs, alternative embodiments may support otherrelationships (e.g., more/fewer VNEs, more/fewer dynamic service chains,multiple different dynamic service chains with some common VNEs and somedifferent VNEs).

The NDs of FIG. 15A, for example, may form part of the Internet or aprivate network; and other electronic devices (not shown; such as enduser devices including workstations, laptops, netbooks, tablets, palmtops, mobile phones, smartphones, phablets, multimedia phones, VoiceOver Internet Protocol (VOIP) phones, terminals, portable media players,GPS units, wearable devices, gaming systems, set-top boxes, Internetenabled household appliances) may be coupled to the network (directly orthrough other networks such as access networks) to communicate over thenetwork (e.g., the Internet or virtual private networks (VPNs) overlaidon (e.g., tunneled through) the Internet) with each other (directly orthrough servers) and/or access content and/or services. Such contentand/or services are typically provided by one or more servers (notshown) belonging to a service/content provider or one or more end userdevices (not shown) participating in a peer-to-peer (P2P) service, andmay include, for example, public webpages (e.g., free content, storefronts, search services), private webpages (e.g., username/passwordaccessed webpages providing email services), and/or corporate networksover VPNs. For instance, end user devices may be coupled (e.g., throughcustomer premise equipment coupled to an access network (wired orwirelessly)) to edge NDs, which are coupled (e.g., through one or morecore NDs) to other edge NDs, which are coupled to electronic devicesacting as servers. However, through compute and storage virtualization,one or more of the electronic devices operating as the NDs in FIG. 15Amay also host one or more such servers (e.g., in the case of the generalpurpose network device 1504, one or more of the software instances1562A-R may operate as servers; the same would be true for the hybridnetwork device 1506; in the case of the special-purpose network device1502, one or more such servers could also be run on a virtualizationlayer executed by the processor(s) 1512); in which case the servers aresaid to be co-located with the VNEs of that ND.

A virtual network is a logical abstraction of a physical network (suchas that in FIG. 15A) that provides network services (e.g., L2 and/or L3services). A virtual network can be implemented as an overlay network(sometimes referred to as a network virtualization overlay) thatprovides network services (e.g., layer 2 (L2, data link layer) and/orlayer 3 (L3, network layer) services) over an underlay network (e.g., anL3 network, such as an Internet Protocol (IP) network that uses tunnels(e.g., generic routing encapsulation (GRE), layer 2 tunneling protocol(L2TP), IPsec) to create the overlay network).

A network virtualization edge (NVE) sits at the edge of the underlaynetwork and participates in implementing the network virtualization; thenetwork-facing side of the NVE uses the underlay network to tunnelframes to and from other NVEs; the outward-facing side of the NVE sendsand receives data to and from systems outside the network. A virtualnetwork instance (VNI) is a specific instance of a virtual network on aNVE (e.g., a NE/VNE on an ND, a part of a NE/VNE on a ND where thatNE/VNE is divided into multiple VNEs through emulation); one or moreVNIs can be instantiated on an NVE (e.g., as different VNEs on an ND). Avirtual access point (VAP) is a logical connection point on the NVE forconnecting external systems to a virtual network; a VAP can be physicalor virtual ports identified through logical interface identifiers (e.g.,a VLAN ID).

Examples of network services include: 1) an Ethernet LAN emulationservice (an Ethernet-based multipoint service similar to an InternetEngineering Task Force (IETF) Multiprotocol Label Switching (MPLS) orEthernet VPN (EVPN) service) in which external systems areinterconnected across the network by a LAN environment over the underlaynetwork (e.g., an NVE provides separate L2 VNIs (virtual switchinginstances) for different such virtual networks, and L3 (e.g., IP/MPLS)tunneling encapsulation across the underlay network); and 2) avirtualized IP forwarding service (similar to IETF IP VPN (e.g., BorderGateway Protocol (BGP)/MPLS IPVPN) from a service definitionperspective) in which external systems are interconnected across thenetwork by an L3 environment over the underlay network (e.g., an NVEprovides separate L3 VNIs (forwarding and routing instances) fordifferent such virtual networks, and L3 (e.g., IP/MPLS) tunnelingencapsulation across the underlay network)). Network services may alsoinclude quality of service capabilities (e.g., traffic classificationmarking, traffic conditioning and scheduling), security capabilities(e.g., filters to protect customer premises from network—originatedattacks, to avoid malformed route announcements), and managementcapabilities (e.g., full detection and processing).

FIG. 15D illustrates a network with a single network element on each ofthe NDs of FIG. 15A, and within this straightforward approach contrastsa traditional distributed approach (commonly used by traditionalrouters) with a centralized approach for maintaining reachability andforwarding information (also called network control), according to someembodiments of the invention. Specifically, FIG. 15D illustrates networkelements (NEs) 1570A-H with the same connectivity as the NDs 1500A-H ofFIG. 15A.

FIG. 15D illustrates that the distributed approach 1572 distributesresponsibility for generating the reachability and forwardinginformation across the NEs 1570A-H; in other words, the process ofneighbor discovery and topology discovery is distributed.

For example, where the special-purpose network device 1502 is used, thecontrol communication and configuration module(s) 1532A-R of the NDcontrol plane 1524 typically include a reachability and forwardinginformation module to implement one or more routing protocols (e.g., anexterior gateway protocol such as Border Gateway Protocol (BGP),Interior Gateway Protocol(s) (IGP) (e.g., Open Shortest Path First(OSPF), Intermediate System to Intermediate System (IS-IS), RoutingInformation Protocol (RIP), Label Distribution Protocol (LDP), ResourceReservation Protocol (RSVP) (including RSVP-Traffic Engineering (TE):Extensions to RSVP for LSP Tunnels and Generalized Multi-Protocol LabelSwitching (GMPLS) Signaling RSVP-TE)) that communicate with other NEs toexchange routes, and then selects those routes based on one or morerouting metrics. Thus, the NEs 1570A-H (e.g., the processor(s) 1512executing the control communication and configuration module(s) 1532A-R)perform their responsibility for participating in controlling how data(e.g., packets) is to be routed (e.g., the next hop for the data and theoutgoing physical NI for that data) by distributively determining thereachability within the network and calculating their respectiveforwarding information. Routes and adjacencies are stored in one or morerouting structures (e.g., Routing Information Base (RIB), LabelInformation Base (LIB), one or more adjacency structures) on the NDcontrol plane 1524. The ND control plane 1524 programs the ND forwardingplane 1526 with information (e.g., adjacency and route information)based on the routing structure(s). For example, the ND control plane1524 programs the adjacency and route information into one or moreforwarding table(s) 1534A-R (e.g., Forwarding Information Base (FIB),Label Forwarding Information Base (LFIB), and one or more adjacencystructures) on the ND forwarding plane 1526. For layer 2 forwarding, theND can store one or more bridging tables that are used to forward databased on the layer 2 information in that data. While the above exampleuses the special-purpose network device 1502, the same distributedapproach 1572 can be implemented on the general purpose network device1504 and the hybrid network device 1506.

FIG. 15D illustrates that a centralized approach 1574 (also known assoftware defined networking (SDN)) that decouples the system that makesdecisions about where traffic is sent from the underlying systems thatforwards traffic to the selected destination. The illustratedcentralized approach 1574 has the responsibility for the generation ofreachability and forwarding information in a centralized control plane1576 (sometimes referred to as a SDN control module, controller, networkcontroller, OpenFlow controller, SDN controller, control plane node,network virtualization authority, or management control entity), andthus the process of neighbor discovery and topology discovery iscentralized. The centralized control plane 1576 has a south boundinterface 1582 with a data plane 1580 (sometime referred to theinfrastructure layer, network forwarding plane, or forwarding plane(which should not be confused with a ND forwarding plane)) that includesthe NEs 1570A-H (sometimes referred to as switches, forwarding elements,data plane elements, or nodes). The centralized control plane 1576includes a network controller 1578, which includes a centralizedreachability and forwarding information module 1579 that determines thereachability within the network and distributes the forwardinginformation to the NEs 1570A-H of the data plane 1580 over the southbound interface 1582 (which may use the OpenFlow protocol). Thus, thenetwork intelligence is centralized in the centralized control plane1576 executing on electronic devices that are typically separate fromthe NDs.

For example, where the special-purpose network device 1502 is used inthe data plane 1580, each of the control communication and configurationmodule(s) 1532A-R of the ND control plane 1524 typically include acontrol agent that provides the VNE side of the south bound interface1582. In this case, the ND control plane 1524 (the processor(s) 1512executing the control communication and configuration module(s) 1532A-R)performs its responsibility for participating in controlling how data(e.g., packets) is to be routed (e.g., the next hop for the data and theoutgoing physical NI for that data) through the control agentcommunicating with the centralized control plane 1576 to receive theforwarding information (and in some cases, the reachability information)from the centralized reachability and forwarding information module 1579(it should be understood that in some embodiments of the invention, thecontrol communication and configuration module(s) 1532A-R, in additionto communicating with the centralized control plane 1576, may also playsome role in determining reachability and/or calculating forwardinginformation—albeit less so than in the case of a distributed approach;such embodiments are generally considered to fall under the centralizedapproach 1574, but may also be considered a hybrid approach).

While the above example uses the special-purpose network device 1502,the same centralized approach 1574 can be implemented with the generalpurpose network device 1504 (e.g., each of the VNE 1560A-R performs itsresponsibility for controlling how data (e.g., packets) is to be routed(e.g., the next hop for the data and the outgoing physical NI for thatdata) by communicating with the centralized control plane 1576 toreceive the forwarding information (and in some cases, the reachabilityinformation) from the centralized reachability and forwardinginformation module 1579; it should be understood that in someembodiments of the invention, the VNEs 1560A-R, in addition tocommunicating with the centralized control plane 1576, may also playsome role in determining reachability and/or calculating forwardinginformation—albeit less so than in the case of a distributed approach)and the hybrid network device 1506. In fact, the use of SDN techniquescan enhance the NFV techniques typically used in the general purposenetwork device 1504 or hybrid network device 1506 implementations as NFVis able to support SDN by providing an infrastructure upon which the SDNsoftware can be run, and NFV and SDN both aim to make use of commodityserver hardware and physical switches.

FIG. 15D also shows that the centralized control plane 1576 has a northbound interface 1584 to an application layer 1586, in which residesapplication(s) 1588. The centralized control plane 1576 has the abilityto form virtual networks 1592 (sometimes referred to as a logicalforwarding plane, network services, or overlay networks (with the NEs1570A-H of the data plane 1580 being the underlay network)) for theapplication(s) 1588. Thus, the centralized control plane 1576 maintainsa global view of all NDs and configured NEs/VNEs, and it maps thevirtual networks to the underlying NDs efficiently (includingmaintaining these mappings as the physical network changes eitherthrough hardware (ND, link, or ND component) failure, addition, orremoval).

The prediction services 1581 can include any number or combinations offunctions related to the prediction system described herein that areimplemented at an application layer 1586.

While FIG. 15D shows the distributed approach 1572 separate from thecentralized approach 1574, the effort of network control may bedistributed differently or the two combined in certain embodiments ofthe invention. For example: 1) embodiments may generally use thecentralized approach (SDN) 1574, but have certain functions delegated tothe NEs (e.g., the distributed approach may be used to implement one ormore of fault monitoring, performance monitoring, protection switching,and primitives for neighbor and/or topology discovery); or 2)embodiments of the invention may perform neighbor discovery and topologydiscovery via both the centralized control plane and the distributedprotocols, and the results compared to raise exceptions where they donot agree. Such embodiments are generally considered to fall under thecentralized approach 1574, but may also be considered a hybrid approach.

While FIG. 15D illustrates the simple case where each of the NDs 1500A-Himplements a single NE 1570A-H, it should be understood that the networkcontrol approaches described with reference to FIG. 15D also work fornetworks where one or more of the NDs 1500A-H implement multiple VNEs(e.g., VNEs 1530A-R, VNEs 1560A-R, those in the hybrid network device1506). Alternatively or in addition, the network controller 1578 mayalso emulate the implementation of multiple VNEs in a single ND.Specifically, instead of (or in addition to) implementing multiple VNEsin a single ND, the network controller 1578 may present theimplementation of a VNE/NE in a single ND as multiple VNEs in thevirtual networks 1592 (all in the same one of the virtual network(s)1592, each in different ones of the virtual network(s) 1592, or somecombination). For example, the network controller 1578 may cause an NDto implement a single VNE (a NE) in the underlay network, and thenlogically divide up the resources of that NE within the centralizedcontrol plane 1576 to present different VNEs in the virtual network(s)1592 (where these different VNEs in the overlay networks are sharing theresources of the single VNE/NE implementation on the ND in the underlaynetwork).

On the other hand, FIGS. 15E and 15F respectively illustrate exemplaryabstractions of NEs and VNEs that the network controller 1578 maypresent as part of different ones of the virtual networks 1592. FIG. 15Eillustrates the simple case of where each of the NDs 1500A-H implementsa single NE 1570A-H (see FIG. 15D), but the centralized control plane1576 has abstracted multiple of the NEs in different NDs (the NEs1570A-C and G-H) into (to represent) a single NE 15701 in one of thevirtual network(s) 1592 of FIG. 15D, according to some embodiments ofthe invention. FIG. 15E shows that in this virtual network, the NE 15701is coupled to NE 1570D and 1570F, which are both still coupled to NE1570E.

FIG. 15F illustrates a case where multiple VNEs (VNE 1570A.1 and VNE1570H.1) are implemented on different NDs (ND 1500A and ND 1500H) andare coupled to each other, and where the centralized control plane 1576has abstracted these multiple VNEs such that they appear as a single VNE1570T within one of the virtual networks 1592 of FIG. 15D, according tosome embodiments of the invention. Thus, the abstraction of a NE or VNEcan span multiple NDs.

While some embodiments of the invention implement the centralizedcontrol plane 1576 as a single entity (e.g., a single instance ofsoftware running on a single electronic device), alternative embodimentsmay spread the functionality across multiple entities for redundancyand/or scalability purposes (e.g., multiple instances of softwarerunning on different electronic devices).

Similar to the network device implementations, the electronic device(s)running the centralized control plane 1576, and thus the networkcontroller 1578 including the centralized reachability and forwardinginformation module 1579, may be implemented a variety of ways (e.g., aspecial purpose device, a general-purpose (e.g., COTS) device, or hybriddevice). These electronic device(s) would similarly includeprocessor(s), a set of one or more physical NIs, and a non-transitorymachine-readable storage medium having stored thereon the centralizedcontrol plane software. For instance, FIG. 16 illustrates, a generalpurpose control plane device 1604 including hardware 1640 comprising aset of one or more processor(s) 1642 (which are often COTS processors)and physical NIs 1646, as well as non-transitory machine readablestorage media 1648 having stored therein centralized control plane (CCP)software 1650.

The non-transitory machine readable storage media 1648 can also havestored therein prediction services 1681. The prediction services 1681can include any number or combinations of functions related to theprediction system described herein. The prediction services 1681 candistributed across multiple general purpose devices 1604 as well asother devices.

In embodiments that use compute virtualization, the processor(s) 1642typically execute software to instantiate a virtualization layer 1654(e.g., in one embodiment the virtualization layer 1654 represents thekernel of an operating system (or a shim executing on a base operatingsystem) that allows for the creation of multiple instances 1662A-Rcalled software containers (representing separate user spaces and alsocalled virtualization engines, virtual private servers, or jails) thatmay each be used to execute a set of one or more applications; inanother embodiment the virtualization layer 1654 represents a hypervisor(sometimes referred to as a virtual machine monitor (VMM)) or ahypervisor executing on top of a host operating system, and anapplication is run on top of a guest operating system within an instance1662A-R called a virtual machine (which in some cases may be considereda tightly isolated form of software container) that is run by thehypervisor ; in another embodiment, an application is implemented as aunikernel, which can be generated by compiling directly with anapplication only a limited set of libraries (e.g., from a libraryoperating system (LibOS) including drivers/libraries of OS services)that provide the particular OS services needed by the application, andthe unikernel can run directly on hardware 1640, directly on ahypervisor represented by virtualization layer 1654 (in which case theunikernel is sometimes described as running within a LibOS virtualmachine), or in a software container represented by one of instances1662A-R). Again, in embodiments where compute virtualization is used,during operation an instance of the CCP software 1650 (illustrated asCCP instance 1676A) is executed (e.g., within the instance 1662A) on thevirtualization layer 1654. In embodiments where compute virtualizationis not used, the CCP instance 1676A is executed, as a unikernel or ontop of a host operating system, on the “bare metal” general purposecontrol plane device 1604. The instantiation of the CCP instance 1676A,as well as the virtualization layer 1654 and instances 1662A-R ifimplemented, are collectively referred to as software instance(s) 1652.

In some embodiments, the CCP instance 1676A includes a networkcontroller instance 1678. The network controller instance 1678 includesa centralized reachability and forwarding information module instance1679 (which is a middleware layer providing the context of the networkcontroller 1578 to the operating system and communicating with thevarious NEs), and an CCP application layer 1680 (sometimes referred toas an application layer) over the middleware layer (providing theintelligence required for various network operations such as protocols,network situational awareness, and user—interfaces). At a more abstractlevel, this CCP application layer 1680 within the centralized controlplane 1576 works with virtual network view(s) (logical view(s) of thenetwork) and the middleware layer provides the conversion from thevirtual networks to the physical view.

The centralized control plane 1576 transmits relevant messages to thedata plane 1580 based on CCP application layer 1680 calculations andmiddleware layer mapping for each flow. A flow may be defined as a setof packets whose headers match a given pattern of bits; in this sense,traditional IP forwarding is also flow—based forwarding where the flowsare defined by the destination IP address for example; however, in otherimplementations, the given pattern of bits used for a flow definitionmay include more fields (e.g., 10 or more) in the packet headers.Different NDs/NEs/VNEs of the data plane 1580 may receive differentmessages, and thus different forwarding information. The data plane 1580processes these messages and programs the appropriate flow informationand corresponding actions in the forwarding tables (sometime referred toas flow tables) of the appropriate NE/VNEs, and then the NEs/VNEs mapincoming packets to flows represented in the forwarding tables andforward packets based on the matches in the forwarding tables.

Standards such as OpenFlow define the protocols used for the messages,as well as a model for processing the packets. The model for processingpackets includes header parsing, packet classification, and makingforwarding decisions. Header parsing describes how to interpret a packetbased upon a well-known set of protocols. Some protocol fields are usedto build a match structure (or key) that will be used in packetclassification (e.g., a first key field could be a source media accesscontrol (MAC) address, and a second key field could be a destination MACaddress).

Packet classification involves executing a lookup in memory to classifythe packet by determining which entry (also referred to as a forwardingtable entry or flow entry) in the forwarding tables best matches thepacket based upon the match structure, or key, of the forwarding tableentries. It is possible that many flows represented in the forwardingtable entries can correspond/match to a packet; in this case the systemis typically configured to determine one forwarding table entry from themany according to a defined scheme (e.g., selecting a first forwardingtable entry that is matched). Forwarding table entries include both aspecific set of match criteria (a set of values or wildcards, or anindication of what portions of a packet should be compared to aparticular value/values/wildcards, as defined by the matchingcapabilities—for specific fields in the packet header, or for some otherpacket content), and a set of one or more actions for the data plane totake on receiving a matching packet. For example, an action may be topush a header onto the packet, for the packet using a particular port,flood the packet, or simply drop the packet. Thus, a forwarding tableentry for IPv4/IPv6 packets with a particular transmission controlprotocol (TCP) destination port could contain an action specifying thatthese packets should be dropped.

Making forwarding decisions and performing actions occurs, based uponthe forwarding table entry identified during packet classification, byexecuting the set of actions identified in the matched forwarding tableentry on the packet.

However, when an unknown packet (for example, a “missed packet” or a“match-miss” as used in OpenFlow parlance) arrives at the data plane1580, the packet (or a subset of the packet header and content) istypically forwarded to the centralized control plane 1576. Thecentralized control plane 1576 will then program forwarding tableentries into the data plane 1580 to accommodate packets belonging to theflow of the unknown packet. Once a specific forwarding table entry hasbeen programmed into the data plane 1580 by the centralized controlplane 1576, the next packet with matching credentials will match thatforwarding table entry and take the set of actions associated with thatmatched entry.

While the invention has been described in terms of several embodiments,those skilled in the art will recognize that the invention is notlimited to the embodiments described, can be practiced with modificationand alteration within the spirit and scope of the appended claims. Thedescription is thus to be regarded as illustrative instead of limiting.

1. A method of a route prediction system utilizing real-time mobilecommunication network data, the method comprising: receiving a routerequest originating from a connected vehicle, the route requestidentifying a route; determining segments for the route; localizingmobile communication network resources to the segments; determining keyperformance indicators for the segments based on at least a currentoffline model; and sending predicted service level indicators (SLIs) forthe segments to the connected vehicle.
 2. The method of claim 1, whereinthe key performance indicators for the segments include mobilecommunication network parameters and real time mobile communicationnetwork data, wherein the real time mobile communication network dataincludes any one or more of coverage area, required quality of service(QoS), current subscriber level, and cell level cellular wireless QoS,and wherein the cell level cellular wireless QoS includes any one ormore of antenna, alarms, past performance, and handover settings.
 3. Themethod of claim 1, wherein the current offline model uses any one ormore of a Digital Terrain Model (DTM) or clutter map.
 4. The method ofclaim 1, further comprising: adjusting the key performance indicatorsbased on current experience data received with the route request.
 5. Themethod of claim 1, further comprising: aggregating the key performanceindicators into the predicted SLIs.
 6. The method of claim 1, whereinthe localizing mobile communication network resources to the segmentsincludes correlating cell towers or geographical grids of networkresources with the segments.
 7. The method of claim 1, wherein thecurrent offline model is selected from a plurality of offline modelsbased on best performance.
 8. The method of claim 1, wherein a mobilecommunication network topology and operation data are correlated withthe route and experience data requests according to any one or more of adistance of mobile communication resources from the route, terrain alongthe route, coverage zones for mobile communication resources, andtraffic on the route.
 9. The method of claim 1, wherein an area of theroute is split into small grids of 10×10 to 30×30 meters and anexperience forecast is made for the small grids is made by the currentoffline model.
 10. The method of claim 1, wherein an experience forecastis generated for each cell tower coverage zone that covers the route,where an approximate probability distribution is made for each cell, andwhere the experience forecast combines these probability distributionsas a weighted average.
 11. The method of claim 1, wherein the keyperformance indicators are linked to the route by location from a set oflatitude longitude pairs (LLPs) defining the route and expected vehiclespeed and distance.
 12. The method of claim 1, wherein when experiencedata includes reports of low quality of service (QoS) for a currentsegment and selected mobile communication network, then the keyperformance indicators is downgraded or similarly adjusted to reflectthe experience data.
 13. A network device to implement a routeprediction system utilizing real-time mobile communication network data,the network device comprising: a non-transitory machine-readable storagemedium having stored therein a route prediction block; and a processorcoupled to the non-transitory machine-readable storage medium, theprocessor to execute the route prediction block, the route predictionblock to receive a route request originating from a connected vehicle,the route request identifying a route, determine segments for the route,localize mobile communication network resources to the segments,determine key performance indicators for the segments based on at leasta current offline model, and send predicted service level indicators(SLIs) for the segments to the connected vehicle. 14-15. (canceled) 16.The network device of claim 13, wherein the key performance indicatorsfor the segments include mobile communication network parameters andreal time mobile communication network data, wherein the real timemobile communication network data includes any one or more of coveragearea, required quality of service (QoS), current subscriber level, andcell level cellular wireless QoS, and wherein the cell level cellularwireless QoS includes any one or more of antenna, alarms, pastperformance, and handover settings.
 17. The network device of claim 13,wherein the current offline model uses any one or more of a DigitalTerrain Model (DTM) or clutter map.
 18. The network device of claim 13,wherein the route prediction block is further to adjust the keyperformance indicators based on current experience data received withthe route request.
 19. The network device of claim 13, wherein the routeprediction block is further to aggregate the key performance indicatorsinto the predicted SLIs.